The segmentation of motion flows within dense crowds of pedestrians in videos is an essential tool in crowd safety and crowd control applications. Videos of crowded scenes can exhibit complex crowd behaviors even under normal situations. For example, crowd flows in large congested areas, such as train stations, can initially appear chaotic. However, is often the case that low dimensional dynamical structures exist in the flow that is desirable to identify and segment from unstructured flows. Moreover, the automatic segmentation of independent crowd flows aids in the monitoring and prediction of hazardous situations in crowded environments.
Of particular interest is the detection and estimation of crowd flows using motion information extracted from the videos. Motion vectors can be determined using optical flow estimation applied to texture, that is pixel intensities, in videos, or the motion vectors can be extracted directly from a bitstream. The bitstream can encoded using any of the well known coding standards, e.g., MPEG, H.264, HEVC, etc.
Considering pedestrians in a crowded scene as particles in a flow, the motion vectors of a video frame correspond to observations of the velocities of particles at a time instance in the flow. Processing motion vectors, instead of the video texture, protects the privacy of individuals observed in surveillance videos.
U.S. Pat. No. 8,773,536 discloses a method for detecting independent motion in a surveillance video. The method constructs a linear system from macroblocks of the video by comparing texture gradients with motion vector. Independent flows are detected when the motion is flagged as a statistical outlier relative to the linear system.
U.S. Pat. No. 8,358,806 discloses a method for crowd segmentation in a video using shape indexing. Background differencing is performed on the video to identify a foreground silhouette shape. Approximate numbers and positions of people are determined by matching the foreground silhouette shape against a set of predetermined foreground silhouette shapes.